Ontologies (What they are; Why you should care; What you should know)

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Ontologies (What they are Why you should care
What you should know)

• Deborah L. McGuinness
• Associate Director and Senior Research Scientist
• Knowledge Systems Laboratory
• Stanford University
• Stanford, CA 94305
• 650-723-9770
• dlm_at_ksl.stanford.edu

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What is an Ontology?
Thesauri narrower term relation
Frames (properties)
General Logical constraints
Formal is-a
Catalog/ ID
Informal is-a
Formal instance
Disjointness, Inverse, part-of
Terms/ glossary
Value Restrs.
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Ontologies and importance to E-Commerce

• Simple ontologies (taxonomies) provide
• Controlled shared vocabulary (search engines,
  authors, users, databases, programs/agents all
  speak same language)
• Site Organization and Navigation Support
• Expectation setting (left side of many web
  pages)
• Umbrella Upper Level Structures (for extension)
• Browsing support (tagged structures such as
  Yahoo!)
• Search support (query expansion approaches such
  as FindUR, e-Cyc)
• Sense disambiguation

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Ontologies and importance to E-Commerce II

• Consistency Checking
• Completion
• Interoperability Support
• Support for validation and verification testing
  (e.g. http//ksl.stanford.edu/projects/DAML/chimae
  ra-jtp-cardinality-test1.daml )
• Configuration support
• Structured, surgical comparative customized
  search
• Generalization/ Specialization
• Foundation for expansion and leverage

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A Few Observations about Ontologies

• Simple ontologies can be built by non-experts
• Veritys Topic Editor, Collaborative Topic
  Builder, GFP, Chimaera, Protégé, OIL-ED, etc.
• Ontologies can be semi-automatically generated
• from crawls of site such as yahoo!, amazon,
  excite, etc.
• Semi-structured sites can provide starting points
• Ontologies are exploding (business pull instead
  of technology push)
• most e-commerce sites are using them - MySimon,
  Amazon, Yahoo! Shopping, VerticalNet, etc.
• Controlled vocabularies (for the web) abound -
  SIC codes, UMLS, UN/SPSC, Open Directory (DMOZ),
  Rosetta Net, SUO
• Business interest expanding ontology directors,
  business ontologies are becoming more complicated
  (roles, value restrictions, ), VC firms
  interested,
• DTDs are making more ontology information
  available
• Markup Languages growing XML, RDF, DAML, RuleML,
  xxML
• Real ontologies are becoming more central to
  applications

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Implications and Needs

• Ontology Language Syntax and Semantics (DAMLOIL)
• Environments for Creation and Maintenance of
  Ontologies
• Training (Conceptual Modeling, reasoning
  implications, )

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Issues

• Collaboration among distributed teams
• Interconnectivity with many systems/standards
• Analysis and diagnosis
• Scale
• Versioning
• Security
• Ease of use
• Diverse training levels /user support
• Presentation style
• Lifecycle
• Extensibility

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Chimaera A Ontology Environment Tool

• An interactive web-based tool aimed at
  supporting
• Ontology analysis (correctness, completeness,
  style, )
• Merging of ontological terms from varied sources
• Maintaining ontologies over time
• Validation of input
• Features multiple I/O languages, loading and
  merging into multiple namespaces, collaborative
  distributed environment support, integrated
  browsing/editing environment, extensible
  diagnostic rule language
• Used in commercial and academic environments
• Available as a hosted service from
  www-ksl-svc.stanford.edu
• Information www.ksl.stanford.edu/software/chima
  era

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Discussion/Conclusion

• Ontologies are exploding core of many
  applications
• Business pull is driving ontology language
  tools and languages
• New generation applications need more expressive
  ontologies and more back end reasoning
• New generation users (the general public) need
  more support than previous users of KRR systems
• Distributed ontologies need more support
  merging, analysis, incompleteness, versioning,
  etc.
• Scale and distribution of the web force mind
  shift
• Everyone is in the game US Government (DARPA,
  NSF, NIST, ), EU, W3C, consortiums, business,
• This is THE time for ontology work!!!

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Some Pointers

• Ontologies Come of Age Paper http//www.ksl.stanf
  ord.edu/people/dlm/papers/ontologies-come-of-age-a
  bstract.html
• Ontologies and Online Commerce Paper
  http//www.ksl.stanford.edu/people/dlm/papers/onto
  logies-and-online-commerce-abstract.html
• DAMLOIL http//www.daml.org/

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Extras

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E-Commerce Search (starting point Forrester
Research modified by McGuinness)

• Ask Queries
• - multiple search interfaces (surgical
  shoppers, advice seekers, window shoppers)
• - set user expectations (interactive query
  refinement)
• - anticipate anomalies
• Get Answers
• - basic information (multiple sorts,
  filtering, structuring)
• - modify results (user defined parameters for
  refining, user profile info, narrow query,
  broaden query, disambiguate query)
• - suggest alternatives (suggest other
  comparable products even from competitors sites)
• Make Decisions
• - manipulate results (enable side by side
  comparison)
• - dive deeper (provide additional info,
  multimedia, other views)
• - take action (buy)

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The Need For KB Analysis

• Large-scale knowledge repositories will
  necessarily contain KBs produced by multiple
  authors in multiple settings
• KBs for applications will typically be built by
  assembling and extending multiple modular KBs
  from repositories that may not be consistent
• KBs developed by multiple authors will frequently
• Express overlapping knowledge in different,
  possibly contradictory ways
• Use differing assumptions and styles
• For such KBs to be used as building blocks -
• They must be reviewed for appropriateness and
  correctness
• That is, they must be analyzed

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Our KB Analysis Task

• Review KBs that
• Were developed using differing standards
• May be syntactically but not semantically
  validated
• May use differing modeling representations
• Produce KB logs (in interactive environments)
• Identify provable problems
• Suggest possible problems in style and/or
  modeling
• Are extensible by being user programmable

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